code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_required(labels)
      c_f.ref_not_supported(embeddings, labels, ref_emb, ref_labels)
      dtype, device = embeddings.dtype, embeddings.device
      self.cast_types(dtype, device)
      miner_weights = lmu.convert_to_weights(
          indices_tuple, labels, dtype=dtype
      ).unsqueeze(1)
      miner_weights = miner_weights - 1
  
      cos = self.get_logits(embeddings)
  
      pos_mask = torch.nn.functional.one_hot(labels, self.num_classes)
      neg_mask = 1 - pos_mask
  
      with_pos_proxies = torch.where(torch.sum(pos_mask, dim=0) != 0)[0]
  
      pos_exp = self.distance.margin(cos, self.margin)
      neg_exp = self.distance.margin(-self.margin, cos)
  
      pos_term = lmu.logsumexp(
          (self.alpha * pos_exp) + miner_weights,
          keep_mask=pos_mask.bool(),
          add_one=True,
          dim=0,
      )
      neg_term = lmu.logsumexp(
          (self.alpha * neg_exp) + miner_weights,
          keep_mask=neg_mask.bool(),
          add_one=True,
          dim=0,
      )
  
      loss_indices = c_f.torch_arange_from_size(self.proxies)
  
      loss_dict = {
          "pos_loss": {
              "losses": pos_term.squeeze(0),
              "indices": loss_indices,
              "reduction_type": "element",
              "divisor": len(with_pos_proxies),
          },
          "neg_loss": {
              "losses": neg_term.squeeze(0),
              "indices": loss_indices,
              "reduction_type": "element",
              "divisor": self.num_classes,
          },
      }
  
      self.add_weight_regularization_to_loss_dict(
          loss_dict, self.proxies
      )
  
      return loss_dict
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/proxy_anchor_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..reducers import DivisorReducer
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
  from .mixins import WeightRegularizerMixin
label_desc: Use image node_id 3 for calling the ProxyAnchorLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: loss_dict